library(data.table)
library(tidyverse)
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library(dplyr)
library(plotly)
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opts_chunk$set(
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Learning Goals

Read in and process the COVID dataset from the New York Times GitHub repository Create interactive graphs of different types using plot_ly() and ggplotly() functions Customize the hoverinfo and other plot features Create a Choropleth map using plot_geo()

Lab Description

We will work with COVID data downloaded from the New York Times. The dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic.

**The objective of this lab is to explore relationships between cases, deaths, and population sizes of US states, and plot data to demonstrate this

Steps

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

1. Read in the data

## data extracted from New York Times state-level data from NYT Github repository
# https://github.com/nytimes/covid-19-data
## state-level population information from us_census_data available on GitHub repository:
# https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data
### FINISH THE CODE HERE ###
# load COVID state-level data from NYT
cv_states <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"))
### FINISH THE CODE HERE ###
# load state population data
state_pops <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv"))
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL
### FINISH THE CODE HERE
cv_states <- merge(cv_states, state_pops, by="state")

2. Look at the data

  • Inspect the dimensions, head, and tail of the data
  • Inspect the structure of each variables. Are they in the correct format?
dim(cv_states)
## [1] 51282     9
head(cv_states)
##     state       date fips   cases deaths geo_id population pop_density abb
## 1 Alabama 2020-10-06    1  160477   2580      1    4887871    96.50939  AL
## 2 Alabama 2021-08-02    1  589110  11536      1    4887871    96.50939  AL
## 3 Alabama 2021-04-13    1  520503  10722      1    4887871    96.50939  AL
## 4 Alabama 2021-06-08    1  546540  11220      1    4887871    96.50939  AL
## 5 Alabama 2021-06-10    1  547135  11252      1    4887871    96.50939  AL
## 6 Alabama 2022-08-30    1 1479605  20048      1    4887871    96.50939  AL
tail(cv_states)
##         state       date fips  cases deaths geo_id population pop_density abb
## 51277 Wyoming 2022-01-28   56 144526   1625     56     577737    5.950611  WY
## 51278 Wyoming 2021-10-19   56  98567   1136     56     577737    5.950611  WY
## 51279 Wyoming 2022-02-17   56 153850   1689     56     577737    5.950611  WY
## 51280 Wyoming 2022-09-13   56 175746   1888     56     577737    5.950611  WY
## 51281 Wyoming 2021-12-31   56 115638   1526     56     577737    5.950611  WY
## 51282 Wyoming 2020-04-27   56    520      7     56     577737    5.950611  WY
str(cv_states)
## 'data.frame':    51282 obs. of  9 variables:
##  $ state      : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
##  $ date       : IDate, format: "2020-10-06" "2021-08-02" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  160477 589110 520503 546540 547135 1479605 551298 1528739 1153149 1531305 ...
##  $ deaths     : int  2580 11536 10722 11220 11252 20048 11358 20505 16826 20533 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : chr  "AL" "AL" "AL" "AL" ...

3. Format the data

  • Make date into a date variable
  • Make state into a factor variable
  • Order the data first by state, second by date
  • Confirm the variables are now correctly formatted
  • Inspect the range values for each variable. What is the date range? The range of cases and deaths?
# format the date
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")
# format the state and state abbreviation (abb) variables
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
abb_list <- unique(cv_states$abb)
cv_states$abb <- factor(cv_states$abb, levels = abb_list)
### FINISH THE CODE HERE 
# order the data first by state, second by date
cv_states = cv_states[order(cv_states$state, cv_states$date),]
# Confirm the variables are now correctly formatted
str(cv_states)
## 'data.frame':    51282 obs. of  9 variables:
##  $ state      : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ date       : Date, format: "2020-03-13" "2020-03-14" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  6 12 23 29 39 51 78 106 131 157 ...
##  $ deaths     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...
head(cv_states)
##       state       date fips cases deaths geo_id population pop_density abb
## 788 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
## 593 Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
## 768 Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
## 334 Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
## 591 Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
## 440 Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL
tail(cv_states)
##         state       date fips  cases deaths geo_id population pop_density abb
## 50536 Wyoming 2022-11-07   56 178866   1914     56     577737    5.950611  WY
## 51197 Wyoming 2022-11-08   56 179366   1917     56     577737    5.950611  WY
## 51249 Wyoming 2022-11-09   56 179366   1917     56     577737    5.950611  WY
## 50945 Wyoming 2022-11-10   56 179366   1917     56     577737    5.950611  WY
## 50446 Wyoming 2022-11-11   56 179366   1917     56     577737    5.950611  WY
## 50817 Wyoming 2022-11-12   56 179366   1917     56     577737    5.950611  WY
# Inspect the range values for each variable. What is the date range? The range of cases and deaths?
head(cv_states)
##       state       date fips cases deaths geo_id population pop_density abb
## 788 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
## 593 Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
## 768 Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
## 334 Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
## 591 Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
## 440 Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL
summary(cv_states)
##            state            date                 fips           cases         
##  Washington   : 1027   Min.   :2020-01-21   Min.   : 1.00   Min.   :       1  
##  Illinois     : 1024   1st Qu.:2020-11-03   1st Qu.:16.00   1st Qu.:   89622  
##  California   : 1023   Median :2021-07-07   Median :29.00   Median :  343328  
##  Arizona      : 1022   Mean   :2021-07-07   Mean   :29.78   Mean   :  815731  
##  Massachusetts: 1016   3rd Qu.:2022-03-11   3rd Qu.:44.00   3rd Qu.:  960676  
##  Wisconsin    : 1012   Max.   :2022-11-12   Max.   :72.00   Max.   :11411720  
##  (Other)      :45158                                                          
##      deaths          geo_id        population        pop_density       
##  Min.   :    0   Min.   : 1.00   Min.   :  577737   Min.   :    1.292  
##  1st Qu.: 1369   1st Qu.:16.00   1st Qu.: 1805832   1st Qu.:   43.659  
##  Median : 5119   Median :29.00   Median : 4468402   Median :  107.860  
##  Mean   :11390   Mean   :29.78   Mean   : 6403921   Mean   :  422.945  
##  3rd Qu.:14412   3rd Qu.:44.00   3rd Qu.: 7535591   3rd Qu.:  229.511  
##  Max.   :97142   Max.   :72.00   Max.   :39557045   Max.   :11490.120  
##                                                     NA's   :975        
##       abb       
##  WA     : 1027  
##  IL     : 1024  
##  CA     : 1023  
##  AZ     : 1022  
##  MA     : 1016  
##  WI     : 1012  
##  (Other):45158
min(cv_states$date)
## [1] "2020-01-21"
max(cv_states$date)
## [1] "2022-11-12"

4. Add new_cases and new_deaths and correct outliers

  • Add variables for new cases, new_cases, and new deaths, new_deaths:
    • Hint: You can set new_cases equal to the difference between cases on date i and date i-1, starting on date i=2
  • Filter to dates after June 1, 2022
  • Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?
  • Correct outliers: Set negative values for new_cases or new_deaths to 0
  • Recalculate cases and deaths as cumulative sum of updated new_cases and new_deaths
  • Get the rolling average of new cases and new deaths to smooth over time
  • Inspect data again interactively
# Add variables for new_cases and new_deaths:
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])
  cv_subset = cv_subset[order(cv_subset$date),]
  # add starting level for new cases and deaths
  cv_subset$new_cases = cv_subset$cases[1]
  cv_subset$new_deaths = cv_subset$deaths[1]
  ### FINISH THE CODE HERE
  for (j in 2:nrow(cv_subset)) {
    cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1]
    cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1]
  }
  # include in main dataset
  cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases
  cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths
}
# Focus on recent dates
cv_states <- cv_states %>% dplyr::filter(date >= "2022-06-01")
### FINISH THE CODE HERE
# Inspect outliers in new_cases using plotly
p1<-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p1)
p1<-NULL # to clear from workspace
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)
p2<-NULL # to clear from workspace
# set negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] = 0
cv_states$new_deaths[cv_states$new_deaths<0] = 0
# Recalculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])
  # add starting level for new cases and deaths
  cv_subset$cases = cv_subset$cases[1]
  cv_subset$deaths = cv_subset$deaths[1]
  ### FINISH CODE HERE
  for (j in 2:nrow(cv_subset)) {
    cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1]
    cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1]
  }
  # include in main dataset
  cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases
  cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths
}
# Smooth new counts
cv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %>% round(digits = 0)
cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %>% round(digits = 0)
# Inspect data again interactively
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)
p2=NULL

5. Add additional variables

  • Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:

    • per100k = cases per 100,000 population
    • newper100k= new cases per 100,000
    • deathsper100k = deaths per 100,000
    • newdeathsper100k = new deaths per 100,000

  • Add a “naive CFR” variable representing deaths / cases on each date for each state

  • Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture

### FINISH CODE HERE
# add population normalized (by 100,000) counts for each variable
cv_states$per100k =  as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))
cv_states$newper100k =  as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
cv_states$deathsper100k =  as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1))
cv_states$newdeathsper100k =  as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1))
# add a naive_CFR variable = deaths / cases
cv_states = cv_states %>% mutate(naive_CFR = round((deaths*100/cases),2))
# create a `cv_states_today` variable
cv_states_today = subset(cv_states, date==max(cv_states$date))

II. Scatterplots

6. Explore scatterplots using plot_ly()

  • Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)
    • Color points by state and size points by state population
    • Use hover to identify any outliers.
    • Remove those outliers and replot.
  • Choose one plot. For this plot:
    • Add hoverinfo specifying the state name, cases per 100k, and deaths per 100k, similarly to how we did this in the lecture notes
    • Add layout information to title the chart and the axes
    • Enable hovermode = "compare"
### FINISH CODE HERE
# pop_density vs. cases
cv_states_today %>% 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
# filter out "District of Columbia"
cv_states_today_filter <- cv_states_today %>% filter(state!="District of Columbia")
# pop_density vs. cases after filtering
cv_states_today_filter %>% 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
# pop_density vs. deathsper100k
cv_states_today_filter %>% 
  plot_ly(x = ~pop_density, y = ~deathsper100k,
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
# Adding hoverinfo
cv_states_today_filter %>% 
  plot_ly(x = ~pop_density, y = ~deathsper100k,
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5),
          hoverinfo = 'text',
          text = ~paste( paste(state, ":", sep=""), 
                paste(" Cases per 100k: ", per100k, sep="") , 
                paste(" Deaths per 100k: ", deathsper100k, sep=""), sep = "<br>")) %>%
  layout(title = "Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",
        yaxis = list(title = "Deaths per 100k"), 
         xaxis = list(title = "Population Density"),
         hovermode = "compare")

7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()

  • For pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly()
  • Explore the pattern between \(x\) and \(y\) using geom_smooth()
    • Explain what you see. Do you think pop_density is a correlate of newdeathsper100k?
### FINISH CODE HERE
p <- ggplot(cv_states_today_filter, aes(x=pop_density, y=deathsper100k, size=population)) + geom_point() + geom_smooth()
ggplotly(p)

8. Multiple line chart

  • Create a line chart of the naive_CFR for all states over time using plot_ly()
    • Use the zoom and pan tools to inspect the naive_CFR for the states that had an increase in September. How have they changed over time?
  • Create one more line chart, for Florida only, which shows new_cases and new_deaths together in one plot. Hint: use add_layer()
    • Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?
### FINISH CODE HERE
# Line chart for naive_CFR for all states over time using `plot_ly()`
plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = "scatter", mode = "lines")
### FINISH CODE HERE
# Line chart for Florida showing new_cases and new_deaths together
cv_states %>% filter(state=="Florida") %>% plot_ly(x = ~date, y = ~new_cases, type = "scatter", mode = "lines") %>% 
  add_lines(x = ~date, y = ~new_deaths, type = "scatter", mode = "lines")

9. Heatmaps

Create a heatmap to visualize new_cases for each state on each date greater than June 1st, 2021 - Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes - Use plot_ly() to create a heatmap out of this matrix. Which states stand out? - Repeat with newper100k variable. Now which states stand out? - Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks

### FINISH CODE HERE
# Map state, date, and new_cases to a matrix
library(tidyr)
cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% dplyr::filter(date>as.Date("2022-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)
# Repeat with newper100k
cv_states_mat <- cv_states %>% select(state, date, newper100k) %>% dplyr::filter(date>as.Date("2022-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)
# Create a second heatmap after filtering to only include dates every other week
filter_dates <- seq(as.Date("2022-06-15"), as.Date("2022-11-01"), by="2 weeks")
cv_states_mat <- cv_states %>% select(state, date, newper100k) %>% filter(date %in% filter_dates)
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)

10. Map

  • Create a map to visualize the naive_CFR by state on October 15, 2021
  • Compare with a map visualizing the naive_CFR by state on most recent date
  • Plot the two maps together using subplot(). Make sure the shading is for the same range of values (google is your friend for this)
  • Describe the difference in the pattern of the CFR.
### For specified date
pick.date = "2022-10-15"
# Extract the data for each state by its abbreviation
cv_per100 <- cv_states %>% 
  filter(date==pick.date) %>%
  select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL
# Create hover text
cv_per100$hover <- with(cv_per100, 
                        paste(state_name, '<br>', 
                    "Cases per 100k: ", newper100k, '<br>',
                    "Cases: ", cases, '<br>', 
                    "Deaths: ", deaths))
# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)
# Make sure both maps are on the same color scale
shadeLimit <- 35
# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~newper100k, 
    text = ~hover, 
    locations = ~state,
    color = ~newper100k, 
    colors = 'Purples'
  )
fig <- fig %>% 
  colorbar(title = "Cases per 100k", limits = c(0,shadeLimit))
fig <- fig %>% 
  layout(
    geo = set_map_details
  )
fig_pick.date <- fig
#############
### Map for today's date
# Extract the data for each state by its abbreviation
cv_per100 <- cv_states_today %>%  
  select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL
# Create hover text
cv_per100$hover <- with(cv_per100, 
            paste(state_name, '<br>', 
            "Cases per 100k: ", newper100k, '<br>',
            "Cases: ", cases, '<br>', 
            "Deaths: ", deaths))
# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~newper100k, 
    text = ~hover, 
    locations = ~state,
    color = ~newper100k, 
    colors = 'Purples'
  )
fig <- fig %>% 
  colorbar(title = "Cases per 100k", limits = c(0,shadeLimit))
fig <- fig %>% 
  layout(
    geo = set_map_details
  )
fig_Today <- fig
### Plot together 
finalfig <- subplot(fig_pick.date, fig_Today, nrows = 2) %>% 
  layout(showlegend = FALSE,
         title = paste('Cases per 100k by State', 
                       '<br>(Hover for value)'),
         hovermode = TRUE
         ) %>%
  colorbar(title = "Cases per 100k", limits = c(0,shadeLimit))
annotations = list( 
  list( 
    x = 0.5,
    y = 0.5,
    text = pick.date,  
    xref = "paper",  
    yref = "paper",  
    xanchor = "center",  
    yanchor = "bottom",  
    showarrow = FALSE   
  ),  
  list( 
    x = 0.5,
    y = -0.05,
    text = Sys.Date(),  
    xref = "paper",  
    yref = "paper",  
    xanchor = "center",  
    yanchor = "bottom",  
    showarrow = FALSE 
  ))
finalfig <- finalfig %>%layout(annotations = annotations) 
finalfig

Ideally, we would not repeat the colorbar twice.